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  • Key AI Trends Across Asia from the HAI 2025 AI Index Report

Key AI Trends Across Asia from the HAI 2025 AI Index Report

Also: Huawei readies new AI chip for mass shipment as China seeks Nvidia alternatives

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AI KATANA
April 21, 2025

Hello!

Today’s newsletter takes you on a fast-paced tour through Asia’s rising dominance in the global AI race, Huawei’s strategic chip breakthrough amidst geopolitical tech tensions, and the looming challenge of AI’s ballooning energy appetite. As the 2025 AI Index highlights, Asia is not only catching up to Western benchmarks but also innovating in deployment and public adoption, particularly in China, South Korea, and India. Meanwhile, Huawei is gearing up to mass-produce its 910C chip—a bold response to U.S. restrictions and Nvidia’s tightening grip on the GPU market. On the energy front, the IEA warns that AI could soon outpace entire nations in power consumption, demanding urgent investment in resilient and sustainable infrastructure. Elsewhere, Anthropic’s new Model Context Protocol could simplify enterprise AI data access, setting new standards for secure, real-time integration. And Bill Gates’ latest musings on AI’s future in professional fields spark essential debates on augmentation versus automation. Whether it’s reshaping research tools, redefining VC flows, or rethinking job security, AI continues to transform every dimension of modern life.

Sliced just for you:

  • 🌏 Key AI Trends Across Asia from the HAI 2025 AI Index Report

  • 💽 Huawei readies new AI chip for mass shipment as China seeks Nvidia alternatives

  • ⚡ Meeting AI Demand as Consumption Doubles by 2030

  • 🔌 Anthropic plugs AI’s data gap with MCP

  • 🧠 Bill Gates AI Prediction: Will Doctors and Lawyers Lose Their Jobs?

🌏 Key AI Trends Across Asia from the HAI 2025 AI Index Report

Asia is rapidly asserting itself as a global leader in AI, according to the 2025 AI Index. China dominates AI research output and patents, while South Korea excels per capita in innovation. Although the US still produces the most influential research and leading AI models, China is quickly closing the performance gap, especially on key benchmarks like MMLU and HumanEval. Government investment is robust across Asia, with China and India spearheading major funding initiatives, and AI is being actively deployed in everyday life, exemplified by Baidu’s robotaxis. Public sentiment toward AI is notably optimistic in Asian nations, contrasting sharply with more skeptical Western views. In healthcare, China leads in AI-related clinical trials, and regional contributions are also shaping model benchmarks and video understanding tools. However, challenges persist, including the spread of AI-generated misinformation during elections. Overall, Asia’s surging investment, research, deployment, and public enthusiasm place it at the forefront of shaping AI’s global trajectory.

Huawei is set to commence mass shipments of its advanced 910C GPU chip next month, marking a strategic move as China seeks alternatives to Nvidia amid tightening U.S. export restrictions. Designed by combining two 910B processors, the 910C achieves computational performance comparable to Nvidia’s H100, doubling both processing power and memory while enhancing support for diverse AI workloads. This launch positions the 910C as a likely go-to chip for Chinese developers in a market disrupted by new U.S. rules requiring export licenses for Nvidia’s H20. While Huawei hasn’t disclosed manufacturing details, it’s believed that China’s SMIC is producing some chip components, though with reportedly low yield rates, and questions remain around TSMC-made chips allegedly linked to the supply chain. As geopolitical tensions reshape the global AI hardware landscape, Huawei and emerging Chinese GPU startups are poised to fill the growing demand domestically.

As global reliance on AI accelerates, its energy footprint is becoming a critical concern. According to the IEA, AI-driven data centers consumed about 415 TWh of electricity in 2024, with usage projected to more than double to 945 TWh by 2030—exceeding Japan’s current national consumption. This growth will stress regional power grids, particularly in the US, where data centers could drive nearly half of electricity demand growth. Meeting this surge will require a diversified energy mix, with renewables, natural gas, and nuclear power playing complementary roles, alongside smarter grid integration and infrastructure upgrades. Yet AI is not just a consumer—it’s also a catalyst, optimizing operations across the energy sector, from forecasting renewable output to reducing industrial energy use and accelerating innovation in clean technologies. Still, realizing these benefits hinges on bridging AI skill gaps, ensuring data and cybersecurity, and fostering collaboration across tech, energy, and policy sectors. The path forward must balance AI’s immense potential with the pressing need for a sustainable and resilient energy future.

🔌 Anthropic plugs AI’s data gap with MCP

Anthropic has launched the Model Context Protocol (MCP), an open standard designed to bridge the gap between large language models and real-time enterprise data without the complexity of custom integrations. Positioned as the “USB-C for AI,” MCP enables models like Claude to access live, task-specific context from sources such as databases and SaaS tools through a lightweight server interface. This approach offers a compliant alternative to exporting sensitive data, attracting adoption from companies like Replit and Sourcegraph. By offering MCP freely, Anthropic aims to drive wider usage of its models while potentially shaping a critical infrastructure layer for generative AI. However, the protocol’s minimalistic design raises security and governance concerns, especially in light of global AI regulations. With early traction among developers and a growing ecosystem of SDKs and adapters, MCP could redefine how enterprise workflows integrate with AI—putting Anthropic at the heart of a data accessibility revolution that may determine which models dominate the next wave of intelligent applications.

Bill Gates recently predicted that AI could perform many tasks done by professionals like doctors and lawyers within the next decade, potentially reshaping how society accesses expertise. However, current trends show AI as an augmentation tool rather than a replacement in these fields. In healthcare, AI aids diagnostics, data analysis, and administrative tasks, but it cannot replicate human empathy, ethical judgment, or handle complex patient interactions. Similarly, in law, AI boosts efficiency in research and document review, yet strategic thinking, advocacy, and client trust remain human strengths. Broader workforce disruption is expected in roles involving repetitive, data-heavy tasks—such as data entry and customer service—while creative, interpersonal, and physical professions are less vulnerable. The future likely hinges on augmentation, not substitution, with professionals leveraging AI for greater productivity. Preparing for this shift involves lifelong learning, reskilling, and ethical oversight to ensure responsible use of AI while retaining the uniquely human elements of work.

🛠️ AI tools updates

As AI becomes integral to academic workflows, researchers are rapidly adopting an ecosystem of tools that streamline literature reviews, generate hypotheses, troubleshoot experiments, write code, and visualize data. Platforms like Google’s Gemini Deep Research and OpenAI’s Deep Research excel at surfacing and synthesizing scientific insights with proper citation, while others such as SciSpace, Claude, and PDF.ai allow users to interrogate and summarize individual papers. Tools like Research Rabbit and CRESt support hypothesis generation and experimental design, while coding aids like GitHub Copilot and Cursor help automate and refine data analysis. Meanwhile, platforms like CatalyzeX enhance reproducibility by surfacing open-source code from academic papers. This shift is transforming research productivity and collaboration, with many students and scientists relying on AI daily, despite ongoing concerns about over-reliance and the need for human oversight.

💵 Venture Capital updates

Between 2013 and 2024, global private investment in AI surpassed $750 billion, with the U.S. dominating the landscape by raising $471 billion—more than the rest of the world combined. China followed with $119 billion, while the UK, Canada, and Israel trailed significantly. This surge in funding has fueled the launch of nearly 7,000 AI startups in the U.S. alone, compared to 1,600 in China and under 900 in the UK. Top investment areas in 2024 included AI infrastructure and governance ($37.3B), data management ($16.6B), and healthcare ($10.8B), driven largely by big players like OpenAI, Anthropic, and xAI. The visualization highlights how substantial funding is not only powering innovation hubs but also reshaping global competitiveness in emerging technologies.

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